activity
20162024
most citedAnomaly detection with Convolutional Graph Neural Networks

96 citations · 333 across the 18 of their papers we have counts for

collaborators

18 papers

hep-ph2024

Non-linear gauge-Higgs CP violation

Akanksha Bhardwaj, Christoph Englert, Dorival Gonçalves +1

A critical element of the LHC physics program is the search for an additional source of CP violation. This is largely unexplored in the context of non-linear Higgs physics, which i…

hep-ph2024★ 3 cited

Foundations of automatic feature extraction at LHC--point clouds and graphs

Akanksha Bhardwaj, Partha Konar, Vishal S. Ngairangbam

Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simula…

hep-ph2024★ 10 cited

Equivariant, Safe and Sensitive -- Graph Networks for New Physics

Akanksha Bhardwaj, Christoph Englert, Wrishik Naskar +2

This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data f…

hep-ph2023

Non-linear top-Higgs CP violation

Akanksha Bhardwaj, Christoph Englert, Dorival Gonçalves +1

Searches for additional sources of CP violation at the Large Hadron Collider are a central part of the Higgs physics programme beyond the Standard Model. Studies employing so-calle…

hep-ph2023★ 15 cited

On the BSM reach of four top production at the LHC

Anisha, Oliver Atkinson, Akanksha Bhardwaj +3

Many scenarios of beyond the Standard Model (BSM) physics give rise to new top-philic interactions that can be probed at proton machines such as the Large Hadron Collider through a…

hep-ph2022

Quartic Gauge-Higgs couplings: Constraints and Future Directions

Anisha, Oliver Atkinson, Akanksha Bhardwaj +2

Constraints on quartic interactions of the Higgs boson with gauge bosons have been obtained by the experimental LHC collaborations focussing on the so-called framework of flat…

hep-ph2022★ 4 cited

Implications of the muon anomalous magnetic moment for the LHC and MUonE

Akanksha Bhardwaj, Christoph Englert, Panagiotis Stylianou

We consider the anomalous magnetic moment of the muon , which shows a significant deviation from the Standard Model expectation given the recent measurements at Fermilab and B…

hep-ph2022★ 24 cited

IRC-safe Graph Autoencoder for unsupervised anomaly detection

Oliver Atkinson, Akanksha Bhardwaj, Christoph Englert +3

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar t…

hep-ph2022★ 22 cited

Discovery prospects of a vectorlike top partner decaying to a singlet boson

Akanksha Bhardwaj, Kartik Bhide, Tanumoy Mandal +2

The possibility of a vectorlike top partner decaying to a new colourless weak-singlet scalar or pseudoscalar has attracted some attention in the literature. We investigate the prod…

hep-ph2022★ 27 cited

Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar

Akanksha Bhardwaj, Tanumoy Mandal, Subhadip Mitra +1

The current experimental data allow for a sub-TeV colourless weak-singlet scalar or pseudoscalar. If such a singlet field is present together with TeV-range vectorlike top and bott…

hep-ph2021★ 17 cited

Machine-enhanced CP-asymmetries in the Higgs sector

Akanksha Bhardwaj, Christoph Englert, Robert Hankache +1

Improving the sensitivity to CP-violation in the Higgs sector is one of the pillars of the precision Higgs programme at the Large Hadron Collider. We present a simple method that a…

hep-ph2021★ 18 cited

Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks

Oliver Atkinson, Akanksha Bhardwaj, Stephen Brown +3

We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional re…

hep-ph2021★ 96 cited

Anomaly detection with Convolutional Graph Neural Networks

Oliver Atkinson, Akanksha Bhardwaj, Christoph Englert +2

We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN auto…

hep-ph2020★ 18 cited

Invisible Higgs search through Vector Boson Fusion: A deep learning approach

Vishal S. Ngairangbam, Akanksha Bhardwaj, Partha Konar +1

Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the…

hep-ph2020★ 8 cited

Boosted jet techniques for a supersymmetric scenario with gravitino LSP

Akanksha Bhardwaj, Juhi Dutta, Partha Konar +2

Search for compressed supersymmetry at multi-TeV scale, in the presence of a light gravitino dark matter, can get sizable uplift while looking into the associated fat-jets with mis…

hep-ph2019★ 31 cited

Probing the inert doublet model using jet substructure with a multivariate analysis

Akanksha Bhardwaj, Partha Konar, Tanumoy Mandal +1

We explore the challenging but phenomenologically interesting hierarchical mass spectrum of the Inert Doublet Model where relatively light dark matter along with much heavier scala…

hep-ph2018★ 35 cited

Looking for Minimal Inverse Seesaw scenarios at the LHC with Jet Substructure Techniques

Akanksha Bhardwaj, Arindam Das, Partha Konar +1

Simple extensions of the Standard Model (SM) with additional Right Handed Neutrinos (RHNs) can elegantly explain the existence of small neutrino masses and their flavor mixings. Co…

hep-ph2016★ 5 cited

Exploring CP phase in -lepton Yukawa coupling in Higgs decays at the LHC

Akanksha Bhardwaj, Partha Konar, Pankaj Sharma +1

We study the prospect of determining the CP violating phase in -lepton Yukawa coupling at the Large Hadron Collider (LHC). While the current run is already exploring the product…